Data-driven optimization for enhanced computational engineering design

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Data-driven science has been contributing to the advancement of several diverse disciplines ranging from biology to finance. In engineering, this has generated renewed interest in machine learning and artificial intelligence, especially in the context of cyber-physical systems that are developed to enable “industry 4.0,” intelligent transportation systems, or “smart” healthcare systems, just to name a few examples. The common theme is the acquisition and analysis of information in the form of data to better understand, model and predict the behaviour of these collaborative cyber-physical systems so that their design can be improved. This “digital twin” paradigm aims at continuously updating the computational models of their physical counterparts using real-time data to (re-)optimize their design, operation, maintenance, repair or replacement, etc.The availability of such rich contextual data can enhance the computational engineering design process significantly by means of optimization. At the same time, several challenges arise that hinder traditional optimization methods from being applied to this new paradigm, e.g.: To be effective, digital twins are likely to high dimensionality; moreover, they are typically blackboxes: gradient information is typically either not available or almost impossible to approximate reliably. Data sets can be quite large and/or sparse, and can include discontinuities and/or outliers. Moreover, they are appended continuously. The required adequacy of the predictive capability of the models can vary significantly in different areas of the input space spanned by design variables and parameters, especially in light of frequent data updates. The interactions among the connected systems have to be captured and coordinated to ensure that the collaborative network is seamlessly integrated and interoperable. This is especially challenging considering that there exist both physical and computational links among the cyber-physical systems.The objective of the proposed research program is to address the above challenges by developing a framework for data-driven computational engineering design optimization in the context of collaborative cyber-physical systems. To accomplish that, we will develop and integrate a data-driven environment for adaptive, adequacy-based multi-model management and validation with rigorous derivative-free optimization algorithms and coordination techniques for distributed systems. The proposed research program will train 5 PhD students, 3 Masters students, and 5 undergraduate students, preparing them for the next generation of engineering and production systems.